Getting Started with RNA Velocity

RNA velocity is based on bridging measurements to a underlying mechanism, mRNA splicing, with two modes indicating the current and future state [RNA velocity—current challenges and future perspectives]. It is a method used to predict the future gene expression of a cell based on the measurement of both spliced and unspliced transcripts of mRNA [2].

RNA velocity could be used to infer the direction of gene expression changes in single-cell RNA sequencing (scRNA-seq) data. It provides insights into the future state of individual cells by using the abundance of unspliced to spliced RNA transcripts. This ratio can indicate the transcriptional dynamics and potential fate of a cell, such as whether it is transitioning from one cell type to another or undergoing differentiation [RNA velocity of single cells].

• velocyto

Velocyto is a package for the analysis of expression dynamics in single cell RNA seq data. In particular, it enables estimations of RNA velocities of single cells by distinguishing unspliced and spliced mRNAs in standard single-cell RNA sequencing protocols. It is the first paper proposed the concept of RNA velocity. velocyto predicted RNA velocity by solving the proposed differential equations for each geneRNA velocity of single cells].

• scVelo

scVelo is a method that solves the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. scVelo was applied to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. scVelo demonstrate the capabilities of the dynamical model on various cell lineages in hippocampal dentate gyrus neurogenesis and pancreatic endocrinogenesis [Generalizing RNA velocity to transient cell states through dynamical modeling].

Here,I will go through the basics of scVelo. The following tutorials go straight into analysis of RNA velocity, latent time, driver identification and many more.

First of all, the input data for scVelo are two count matrices of pre-mature (unspliced) and mature (spliced) abundances, which were obtained from standard sequencing protocols, using the velocyto.

#!pip install numpy==1.23.2 pandas==1.5.3 matplotlib==3.7.3 scanpy==1.9.6 igraph==0.9.8 scvelo==0.2.5 loompy==3.0.6 anndata==0.8.0

#!pip install tqdm 
#!pip install ipywidgets

import numpy as np
import pandas as pd
import matplotlib.pyplot as pl
import scanpy as sc
import igraph
import scvelo as scv
import loompy as lmp
import anndata

import warnings
warnings.filterwarnings('ignore')

# Set parameters for plots, including size, color, etc.
scv.settings.verbosity = 3  # show errors(0), warnings(1), info(2), hints(3)
scv.settings.presenter_view = True  # set max width size for presenter view
scv.set_figure_params('scvelo')  # for beautified visualization

adata = scv.read('/home/aruna/Desktop/Analysis/Project1/Data/Project1_merged.h5ad', cache=True)
adata

AnnData object with n_obs × n_vars = 46110 × 1374
    obs: 'batch', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'leiden', 'condition', 'cell_type'
    var: 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'batch_colors', 'cell_type_colors', 'condition_colors', 'dendrogram_leiden', 'hvg', 'leiden', 'leiden_colors', 'leiden_sizes', 'log1p', 'neighbors', 'paga', 'pca', 'rank_genes_groups', 'umap'
    obsm: 'X_pca', 'X_umap', 'ora_estimate', 'ora_pvals'
    varm: 'PCs'
    layers: 'counts'
    obsp: 'connectivities', 'distances'

ldata = scv.read('/home/aruna/Desktop/Analysis/Project1/Data/Project1_loomp.loom', cache=True)
ldata

AnnData object with n_obs × n_vars = 46529 × 20070
    var: 'Accession', 'Chromosome', 'End', 'Start', 'Strand'
    layers: 'ambiguous', 'matrix', 'spliced', 'unspliced'

scv.utils.clean_obs_names(adata)
scv.utils.clean_obs_names(ldata)
adata = scv.utils.merge(adata, ldata)

adata

AnnData object with n_obs × n_vars = 46110 × 1229
    obs: 'batch', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'leiden', 'condition', 'cell_type', 'sample_batch', 'initial_size_unspliced', 'initial_size_spliced', 'initial_size'
    var: 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std', 'Accession', 'Chromosome', 'End', 'Start', 'Strand'
    uns: 'batch_colors', 'cell_type_colors', 'condition_colors', 'dendrogram_leiden', 'hvg', 'leiden', 'leiden_colors', 'leiden_sizes', 'log1p', 'neighbors', 'paga', 'pca', 'rank_genes_groups', 'umap'
    obsm: 'X_pca', 'X_umap', 'ora_estimate', 'ora_pvals'
    varm: 'PCs'
    layers: 'counts', 'ambiguous', 'matrix', 'spliced', 'unspliced'
    obsp: 'connectivities', 'distances'

adata.layers

Layers with keys: counts, ambiguous, matrix, spliced, unspliced

scv.pl.proportions(adata, groupby="cell_type")

Here, the proportions of spliced/unspliced counts are displayed. Depending on the protocol used (Drop-Seq, Smart-Seq, inDrop and 10x Genomics Chromium protocols), we typically have between 10%-25% of unspliced molecules containing intronic sequences. We also advice you to examine the variations on cluster level to verify consistency in splicing efficiency. Here, we find variations, with slightly lower unspliced proportions at Ciliated & Merkels cells, then *higher proportion* at *Mesangial & Keratinocytes cells*.

scv.pl.proportions(adata, groupby="condition")

sc.pl.embedding(adata, basis="umap", color=["condition", "cell_type"])

Estimate RNA velocity

RNA velocity estimation can currently be tackled with three existing approaches:

• steady-state / deterministic model (using steady-state residuals)

• stochastic model (using second-order moments),

• dynamical model (using a likelihood-based framework).

• The steady-state / deterministic model:, as being used in velocyto, estimates velocities as follows: Under the assumption that transcriptional phases (induction and repression) last sufficiently long to reach a steady-state equilibrium (active and inactive),velocities are quantified as the deviation of the observed ratio from its steady-state ratio. The equilibrium mRNA levels are approximated with a linear regression on the presumed steady states in the lower and upper quantiles. This simplification makes two fundamental assumptions: a common splicing rate across genes and steady-state mRNA levels to be reflected in the data. It can lead to errors in velocity estimates and cellular states as the assumptions are often violated, in particular when a population comprises multiple heterogeneous subpopulation dynamics.

• The stochastic model: aims to better capture the steady states. By treating transcription, splicing and degradation as probabilistic events, the resulting Markov process is approximated by moment equations. By including secondorder moments, it exploits not only the balance of unspliced to spliced mRNA levels but also their covariation. It has been demonstrated on the endocrine pancreas that stochasticity adds valuable information, overall yielding higher consistency than the deterministic model, while remaining as efficient in computation time.

• The dynamical model: (most powerful while computationally most expensive) solves the full dynamics of splicing kinetics for each gene. It thereby adapts RNA velocity to widely varying specifications such as non-stationary populations, as does not rely on the restrictions of a common splicing rate or steady states to be sampled.

  The splicing dynamics

  𝑑𝑢(𝑡)/𝑑𝑡 = 𝛼𝑘(𝑡) − 𝛽𝑢(𝑡), (4.1)

  𝑑𝑠(𝑡)/𝑑𝑡 = 𝛽𝑢(𝑡) − 𝛾𝑠(4.2) (𝑡),

  is solved in a likelihood-based expectation-maximization framework, by iteratively estimating the parameters of reaction rates and latent cell-specific variables, i.e. transcriptional state k and cell-internal latent time t.It thereby aims to learn the unspliced/spliced phase trajectory. Four transcriptional states are modeled to account for all possible configurations of gene activity: two dynamic transient states (induction and repression) and two steady states (active and inactive) potentially reached after each dynamic transition.

scv.pp.filter_genes(adata, min_shared_counts=20)
scv.pp.normalize_per_cell(adata)
scv.pp.filter_genes_dispersion(adata, n_top_genes=2000)
scv.pp.log1p(adata)

Filtered out 119 genes that are detected 20 counts (shared).
WARNING: Did not normalize X as it looks processed already. To enforce normalization, set `enforce=True`.
Normalized count data: spliced, unspliced.
Skip filtering by dispersion since number of variables are less than `n_top_genes`.

scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)

Filtered out 8 genes that are detected 20 counts (shared).
WARNING: Did not normalize X as it looks processed already. To enforce normalization, set `enforce=True`.
WARNING: Did not normalize spliced as it looks processed already. To enforce normalization, set `enforce=True`.
WARNING: Did not normalize unspliced as it looks processed already. To enforce normalization, set `enforce=True`.
Skip filtering by dispersion since number of variables are less than `n_top_genes`.
WARNING: Did not modify X as it looks preprocessed already.
computing neighbors
    finished (0:00:16) --> added 
    'distances' and 'connectivities', weighted adjacency matrices (adata.obsp)
computing moments based on connectivities
    finished (0:00:03) --> added 
    'Ms' and 'Mu', moments of un/spliced abundances (adata.layers)

scv.tl.recover_dynamics(adata)

scv.tl.velocity(adata, mode='dynamical', n_jobs=4)
scv.tl.velocity_graph(adata)

computing velocities
WARNING: Falling back to stochastic model. For the dynamical model run tl.recover_dynamics first.
    finished (0:00:04) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)
computing velocity graph (using 1/16 cores)

  0%|          | 0/46110 [00:00<?, ?cells/s]

    finished (0:00:57) --> added 
    'velocity_graph', sparse matrix with cosine correlations (adata.uns)

adata.write('/home/aruna/Desktop/Analysis/Project1/Data/Project1&DynVelo.h5ad', compression='gzip')

adata = scv.read('/home/aruna/Desktop/Analysis/Project1/Data/Project1&DynVelo.h5ad', cache=True)

adata

AnnData object with n_obs × n_vars = 46110 × 1102
    obs: 'batch', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'leiden', 'condition', 'cell_type', 'sample_batch', 'initial_size_unspliced', 'initial_size_spliced', 'initial_size', 'n_counts', 'velocity_self_transition', 'velocity_length', 'velocity_confidence', 'velocity_confidence_transition', 'root_cells', 'end_points', 'velocity_pseudotime'
    var: 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std', 'Accession', 'Chromosome', 'End', 'Start', 'Strand', 'fit_r2', 'fit_alpha', 'fit_beta', 'fit_gamma', 'fit_t_', 'fit_scaling', 'fit_std_u', 'fit_std_s', 'fit_likelihood', 'fit_u0', 'fit_s0', 'fit_pval_steady', 'fit_steady_u', 'fit_steady_s', 'fit_variance', 'fit_alignment_scaling', 'velocity_genes', 'spearmans_score', 'velocity_score'
    uns: 'batch_colors', 'cell_type_colors', 'cell_type_sizes', 'condition_colors', 'dendrogram_leiden', 'hvg', 'leiden', 'leiden_colors', 'leiden_sizes', 'log1p', 'neighbors', 'paga', 'pca', 'rank_genes_groups', 'rank_velocity_genes', 'recover_dynamics', 'umap', 'velocity_graph', 'velocity_graph_neg', 'velocity_params'
    obsm: 'X_pca', 'X_umap', 'ora_estimate', 'ora_pvals', 'velocity_umap'
    varm: 'PCs', 'loss'
    layers: 'Ms', 'Mu', 'ambiguous', 'counts', 'fit_t', 'fit_tau', 'fit_tau_', 'matrix', 'spliced', 'unspliced', 'velocity', 'velocity_u'
    obsp: 'connectivities', 'distances'

print(adata.var['velocity_genes'].sum(), adata.n_vars)
top_genes = adata.var_names[adata.var.fit_likelihood.argsort()[::-1]]
scv.pl.scatter(adata, basis=top_genes[:10], ncols=4, color='cell_type', wspace=.5, hspace=.5)

465 1102

There are 465 genes being used and 1102 cells.

Project the velocities

scv.pl.velocity_embedding_stream(adata, basis='umap', color=['condition','cell_type'], 
                                 legend_loc='right margin')

scv.pl.velocity_embedding_grid(adata, basis='umap', color=['condition', 'cell_type'], 
                          legend_loc='right margin')

Interprete the velocities

See the gif here to get an idea of how to interpret a spliced vs. unspliced phase portrait. Gene activity is orchestrated by transcriptional regulation. Transcriptional induction for a particular gene results in an increase of (newly transcribed) precursor unspliced mRNAs while, conversely, repression or absence of transcription results in a decrease of unspliced mRNAs. Spliced mRNAs is produced from unspliced mRNA and follows the same trend with a time lag. Time is a hidden/latent variable. Thus, the dynamics needs to be inferred from what is actually measured: spliced and unspliced mRNAs as displayed in the phase portrait.

adata

AnnData object with n_obs × n_vars = 46110 × 1102
    obs: 'batch', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'leiden', 'condition', 'cell_type', 'sample_batch', 'initial_size_unspliced', 'initial_size_spliced', 'initial_size', 'n_counts', 'velocity_self_transition', 'velocity_length', 'velocity_confidence', 'velocity_confidence_transition', 'root_cells', 'end_points', 'velocity_pseudotime'
    var: 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std', 'Accession', 'Chromosome', 'End', 'Start', 'Strand', 'fit_r2', 'fit_alpha', 'fit_beta', 'fit_gamma', 'fit_t_', 'fit_scaling', 'fit_std_u', 'fit_std_s', 'fit_likelihood', 'fit_u0', 'fit_s0', 'fit_pval_steady', 'fit_steady_u', 'fit_steady_s', 'fit_variance', 'fit_alignment_scaling', 'velocity_genes', 'spearmans_score', 'velocity_score'
    uns: 'batch_colors', 'cell_type_colors', 'cell_type_sizes', 'condition_colors', 'dendrogram_leiden', 'hvg', 'leiden', 'leiden_colors', 'leiden_sizes', 'log1p', 'neighbors', 'paga', 'pca', 'rank_genes_groups', 'rank_velocity_genes', 'recover_dynamics', 'umap', 'velocity_graph', 'velocity_graph_neg', 'velocity_params'
    obsm: 'X_pca', 'X_umap', 'ora_estimate', 'ora_pvals', 'velocity_umap'
    varm: 'PCs', 'loss'
    layers: 'Ms', 'Mu', 'ambiguous', 'counts', 'fit_t', 'fit_tau', 'fit_tau_', 'matrix', 'spliced', 'unspliced', 'velocity', 'velocity_u'
    obsp: 'connectivities', 'distances'

df = scv.get_df(adata, 'rank_genes_groups/names')
df.head(10)

	0
0	HNRNPH1
1	CTNNB1
2	SET
3	YWHAE
4	EIF5A
5	PPP1CB
6	B4GALT1
7	CALR
8	CDC42
9	EIF2S3

scv.pl.velocity(adata, ['HNRNPH1',  'CTNNB1', 'SET', 'YWHAE', 'EIF5A'], color='cell_type')

Identify important genes

• By Cell type

scv.tl.rank_velocity_genes(adata, groupby='cell_type', min_corr=.3)

df = scv.DataFrame(adata.uns['rank_velocity_genes']['names'])
df.head(10)

ranking velocity genes
    finished (0:00:03) --> added 
    'rank_velocity_genes', sorted scores by group ids (adata.uns) 
    'spearmans_score', spearmans correlation scores (adata.var)

	B cells	Basophils	Cholangiocytes	Ciliated cells	Eosinophils	Gamma delta T cells	Ionocytes	Keratinocytes	Merkel cells	Mesangial cells	Monocytes	Pancreatic progenitor cells	Parathyroid chief cells
0	PLSCR1	DDX60L	MFGE8	UBB	TRIM21	PIF1	EPHX1	DDX3Y	EIF2S3	MYL6	TRIM21	LGALS3BP	PDLIM7
1	EIF2AK2	RNF213	IGFBP4	NCL	NLRC5	DMKN	DMKN	CDC20	KDSR	AVPI1	IFIT1	KDSR	DMKN
2	DMKN	ARHGAP29	SCD	SNRPD1	PSMB9	TESC	TPD52L1	PDLIM7	ANKRD29	NDRG1	OPTN	MYLK	PLEK2
3	NDRG1	PRKD2	TRIM56	SNRPB	CXXC5	VCAN	ULK1	RSPO3	MYLK	APOC1	LAP3	AKR1C3	CDA
4	F2RL1	PARP14	FOSL2	MYL6	CRIP1	BASP1	SLC7A11	RPS6KA5	SLC12A2	EIF1AY	C15orf48	LAMB1	EIF1AY
5	RSPO3	DTX3L	PPP1R14A	PPP1R14A	LAP3	LMO7	RSPO3	DMKN	CALR	NCOA3	PSMB9	PLSCR1	RSPO3
6	DPYSL3	RIPK2	TBX3	SSBP1	C15orf48	GPRC5A	OGFR	SLC25A28	TRIM56	LYPD1	CD38	DCBLD2	EPHX1
7	TPD52L1	PLSCR1	KDSR	HSP90B1	ZC3HAV1	DDX3Y	FA2H	VCAN	SLC3A2	RIPK2	CCND3	IFI35	NDRG1
8	SEMA3A	ZNFX1	NUAK1	PSMB3	IRS2	SMIM14	VCAN	NCOA3	MMD	COL5A2	MAP3K8	RNF213	NCEH1
9	EIF1AY	NLRC5	HSPG2	MGLL	DTX3L	PAPPA	COL4A1	SEMA3A	C21orf58	SULT2B1	CRIP1	SULT2B1	KLF7

for cell_type in ['Gamma delta T cells', 'Eosinophils', 'Monocytes']:
    scv.pl.scatter(adata, df[cell_type][:5], ylabel=cell_type, color='cell_type', wspace=.6)

for cell_type in ['Gamma delta T cells', 'Eosinophils', 'Monocytes']:
    scv.pl.velocity(adata, df[cell_type][:3], color='cell_type')

• By Condition

scv.tl.rank_velocity_genes(adata, groupby='condition', min_corr=.3)

df = scv.DataFrame(adata.uns['rank_velocity_genes']['names'])
df.head(10)

ranking velocity genes
    finished (0:00:00) --> added 
    'rank_velocity_genes', sorted scores by group ids (adata.uns) 
    'spearmans_score', spearmans correlation scores (adata.var)

	Control	Trait
0	NUAK2	LAP3
1	CASP7	PSMB9
2	STRA6	TRIM21
3	TPD52L1	ISG20
4	HELZ2	NLRC5
5	PLSCR1	C15orf48
6	SLC25A28	OAS2
7	EPHX1	OPTN
8	IL15	IFI35
9	SPOCK1	SAMHD1

for condition in ['Control', 'Trait']:
    scv.pl.scatter(adata, df[condition][:5], ylabel=condition, color='cell_type', wspace=.6)

for condition in ['Control', 'Trait']:
    scv.pl.velocity(adata, df[condition][:5], color='cell_type')

Speed and coherence

Two more useful stats: - The speed or rate of differentiation is given by the length of the velocity vector. - The coherence of the vector field (i.e., how a velocity vector correlates with its neighboring velocities) provides a measure of confidence.

scv.tl.velocity_confidence(adata)
keys = 'velocity_length', 'velocity_confidence'
scv.pl.scatter(adata, c=keys, cmap='coolwarm', perc=[5, 95])

--> added 'velocity_length' (adata.obs)
--> added 'velocity_confidence' (adata.obs)

df = adata.obs.groupby('cell_type')[keys].mean().T
df.style.background_gradient(cmap='coolwarm', axis=1)

cell_type	B cells	Basophils	Cholangiocytes	Ciliated cells	Eosinophils	Gamma delta T cells	Ionocytes	Keratinocytes	Merkel cells	Mesangial cells	Monocytes	Pancreatic progenitor cells	Parathyroid chief cells
velocity_length	9.816980	10.213184	9.200196	9.103175	10.341850	9.662992	9.965525	9.179878	8.436419	10.732821	9.416450	10.338098	9.709996
velocity_confidence	0.843679	0.852764	0.851790	0.785651	0.845935	0.824034	0.837549	0.826001	0.850492	0.842678	0.882559	0.846927	0.823195

df = adata.obs.groupby('condition')[keys].mean().T
df.style.background_gradient(cmap='coolwarm', axis=1)

condition	Control	Trait
velocity_length	9.405773	9.794776
velocity_confidence	0.837093	0.854158

Velocity graph and pseudotime

We can visualize the velocity graph to portray all velocity-inferred cell-to-cell connections/transitions. It can be confined to high-probability transitions by setting a threshold.

scv.pl.velocity_graph(adata, threshold=.5, color='cell_type', 
                          legend_loc='right margin', ncols=1)

#x, y = scv.utils.get_cell_transitions(adata, basis='umap', n_neighbors=10, starting_cell=70)
#ax = scv.pl.velocity_graph(adata, c='lightgrey', edge_width=.05, show=False)
#ax = scv.pl.scatter(adata, x=x, y=y, s=120, c='ascending', cmap='gnuplot', ax=ax)

Finally, based on the velocity graph, a velocity pseudotime can be computed. After inferring a distribution over root cells from the graph, it measures the average number of steps it takes to reach a cell after walking along the graph starting from the root cells.

scv.tl.velocity_pseudotime(adata)
scv.pl.scatter(adata, color='velocity_pseudotime', cmap='gnuplot')

PAGA velocity graph

PAGA graph abstraction has benchmarked as top-performing method for trajectory inference. It provides a graph-like map of the data topology with weighted edges corresponding to the connectivity between two clusters. Here, PAGA is extended by velocity-inferred directionality.

adata

AnnData object with n_obs × n_vars = 46110 × 1102
    obs: 'batch', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'leiden', 'condition', 'cell_type', 'sample_batch', 'initial_size_unspliced', 'initial_size_spliced', 'initial_size', 'n_counts', 'velocity_self_transition', 'velocity_length', 'velocity_confidence', 'velocity_confidence_transition', 'root_cells', 'end_points', 'velocity_pseudotime'
    var: 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std', 'Accession', 'Chromosome', 'End', 'Start', 'Strand', 'fit_r2', 'fit_alpha', 'fit_beta', 'fit_gamma', 'fit_t_', 'fit_scaling', 'fit_std_u', 'fit_std_s', 'fit_likelihood', 'fit_u0', 'fit_s0', 'fit_pval_steady', 'fit_steady_u', 'fit_steady_s', 'fit_variance', 'fit_alignment_scaling', 'velocity_genes', 'spearmans_score', 'velocity_score'
    uns: 'batch_colors', 'cell_type_colors', 'cell_type_sizes', 'condition_colors', 'dendrogram_leiden', 'hvg', 'leiden', 'leiden_colors', 'leiden_sizes', 'log1p', 'neighbors', 'paga', 'pca', 'rank_genes_groups', 'rank_velocity_genes', 'recover_dynamics', 'umap', 'velocity_graph', 'velocity_graph_neg', 'velocity_params'
    obsm: 'X_pca', 'X_umap', 'ora_estimate', 'ora_pvals', 'velocity_umap'
    varm: 'PCs', 'loss'
    layers: 'Ms', 'Mu', 'ambiguous', 'counts', 'fit_t', 'fit_tau', 'fit_tau_', 'matrix', 'spliced', 'unspliced', 'velocity', 'velocity_u'
    obsp: 'connectivities', 'distances'

# PAGA requires to install igraph, if not done yet.
!pip install python-igraph --upgrade --quiet

# this is needed due to a current bug - bugfix is coming soon.
adata.uns['neighbors']['distances'] = adata.obsp['distances']
adata.uns['neighbors']['connectivities'] = adata.obsp['connectivities']

scv.tl.paga(adata, groups='cell_type', use_time_prior=False)
df = scv.get_df(adata, 'paga/transitions_confidence', precision=2).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')

running PAGA 
    finished (0:00:03) --> added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns)
    'paga/transitions_confidence', velocity transitions (adata.uns)

	B cells	Basophils	Cholangiocytes	Ciliated cells	Eosinophils	Gamma delta T cells	Ionocytes	Keratinocytes	Merkel cells	Mesangial cells	Monocytes	Pancreatic progenitor cells	Parathyroid chief cells
B cells	0	0	0	0	0	0	0	0.46	0	0	0	0	0
Basophils	0	0	0	0	0	0	0	0	0	0	0.43	0	0
Cholangiocytes	0	0	0	0.025	0	0	0	0	0	0	0	0.32	0
Ciliated cells	0	0	0	0	0	0.16	0	0	0	0	0	0	0
Eosinophils	0	0	0	0	0	0	0	0	0	0	0.31	0	0
Gamma delta T cells	0	0	0	0	0	0	0	0	0	0	0	0	0
Ionocytes	0	0	0	0	0	0	0	0	0	0	0	0	0
Keratinocytes	0	0	0	0	0	0.22	0	0	0	0	0	0	0
Merkel cells	0	0.23	0	0	0	0	0	0	0	0	0	0	0
Mesangial cells	0	0	0	0	0	0	0	0.21	0	0	0	0	0
Monocytes	0	0	0	0	0	0	0	0	0	0	0	0	0
Pancreatic progenitor cells	0	0	0	0	0	0	0	0	0	0	0.35	0	0
Parathyroid chief cells	0	0	0	0	0	0	0.47	0.69	0	0	0	0	0

This reads from left/row to right/column, thus e.g. assigning a confident transition from Merkel sells to Basophils.

This table can be summarized by a directed graph superimposed onto the UMAP embedding.

scv.pl.paga(adata, basis='umap', size=50, alpha=.5, 
            min_edge_width=2, node_size_scale=1)

WARNING: Invalid color key. Using grey instead.

scv.pl.velocity_embedding_stream(adata, basis='umap', color='cell_type', 
                                 legend_loc='right margin', ncols=1)

Here we observb 2 main velocity direction, one in Gamma delta T cells and Monocytes.

results_file = '/home/aruna/Desktop/Analysis/Project1/Data/Project1&DynVelo.h5ad'  
adata.write(results_file)

Some more analysis for dynamical mode

Kinetic rate paramters

The rates of RNA transcription, splicing and degradation are estimated without the need of any experimental data.

They can be useful to better understand the cell identity and phenotypic heterogeneity.

df = adata.var
df = df[(df['fit_likelihood'] > .1) & df['velocity_genes'] == True]

kwargs = dict(xscale='log', fontsize=16)
with scv.GridSpec(ncols=3) as pl:
    pl.hist(df['fit_alpha'], xlabel='transcription rate', **kwargs)
    pl.hist(df['fit_beta'] * df['fit_scaling'], xlabel='splicing rate', xticks=[.1, .4, 1], **kwargs)
    pl.hist(df['fit_gamma'], xlabel='degradation rate', xticks=[.1, .4, 1], **kwargs)

scv.get_df(adata, 'fit*', dropna=True).head()

	fit_r2	fit_alpha	fit_beta	fit_gamma	fit_t_	fit_scaling	fit_std_u	fit_std_s	fit_likelihood	fit_u0	fit_s0	fit_pval_steady	fit_steady_u	fit_steady_s	fit_variance	fit_alignment_scaling
SAMD11	0.12	0.34	3.22	0.21	10.09	0.11	0.03	0.32	1.86e-01	0.0	0.0	0.45	0.10	1.03	1.17	2.89
DHRS3	0.39	0.19	0.32	0.17	20.01	0.36	0.19	0.24	2.55e-01	0.0	0.0	0.35	0.52	0.72	0.68	3.70
PADI2	0.22	0.23	1.60	0.29	4.71	0.34	0.03	0.11	1.31e-47	0.0	0.0	0.47	0.12	0.45	2.14	1.89
CDA	0.22	0.42	5.31	0.38	8.99	0.10	0.03	0.30	1.58e-01	0.0	0.0	0.45	0.09	0.94	1.43	1.93
RAP1GAP	0.12	0.24	2.36	0.24	5.83	0.20	0.03	0.17	1.33e-01	0.0	0.0	0.45	0.09	0.58	1.72	2.36

The estimated gene-specific parameters comprise rates of transription (fit_alpha), splicing (fit_beta), degradation (fit_gamma), switching time point (fit_t_), a scaling parameter to adjust for under-represented unspliced reads (fit_scaling), standard deviation of unspliced and spliced reads (fit_std_u, fit_std_s), the gene likelihood (fit_likelihood), inferred steady-state levels (fit_steady_u, fit_steady_s) with their corresponding p-values (fit_pval_steady_u, fit_pval_steady_s), the overall model variance (fit_variance), and a scaling factor to align the gene-wise latent times to a universal, gene-shared latent time (fit_alignment_scaling).

Latent time

The dynamical model recovers the latent time of the underlying cellular processes. This latent time represents the cell’s internal clock and approximates the real time experienced by cells as they differentiate, based only on its transcriptional dynamics.

scv.tl.latent_time(adata)
scv.pl.scatter(adata, color='latent_time', color_map='gnuplot', size=80)

computing latent time using root_cells as prior
    finished (0:00:09) --> added 
    'latent_time', shared time (adata.obs)

top_genes = adata.var['fit_likelihood'].sort_values(ascending=False).index[:300]
scv.pl.heatmap(adata, var_names=top_genes, sortby='latent_time', col_color='cell_type', n_convolve=100)

Top-likelihood genes

Driver genes display pronounced dynamic behavior and are systematically detected via their characterization by high likelihoods in the dynamic model.

top_genes = adata.var['fit_likelihood'].sort_values(ascending=False).index
scv.pl.scatter(adata, basis=top_genes[:15], ncols=5, frameon=False, color='cell_type')

Cluster-specific top-likelihood genes

Moreover, partial gene likelihoods can be computed for a each cluster of cells to enable cluster-specific identification of potential drivers.

scv.tl.rank_dynamical_genes(adata, groupby='cell_type')
df = scv.get_df(adata, 'rank_dynamical_genes/names')
df.head(10)

ranking genes by cluster-specific likelihoods
    finished (0:00:09) --> added 
    'rank_dynamical_genes', sorted scores by group ids (adata.uns)

	B cells	Basophils	Cholangiocytes	Ciliated cells	Eosinophils	Gamma delta T cells	Ionocytes	Keratinocytes	Merkel cells	Mesangial cells	Monocytes	Pancreatic progenitor cells	Parathyroid chief cells
0	YWHAE	YWHAE	CALR	TPX2	IFI6	YWHAE	YWHAE	YWHAE	CALR	YWHAE	YWHAE	YWHAE	YWHAE
1	CALR	CALR	DCBLD2	KCNMA1	EIF2AK2	ANTXR1	FOSL1	TOP2A	YWHAE	CALR	IFI6	CALR	CALR
2	DCBLD2	TPX2	YWHAE	NXN	STAT1	HPCAL1	CALR	ADAR	DCBLD2	FLNB	CALR	PARP9	FOSL1
3	TOP2A	PLSCR1	CYP24A1	CYP24A1	NLRC5	HPGD	ETS2	ETS2	ZFP36L1	AXL	NLRC5	TOP2A	KCNMA1
4	ANTXR1	DCBLD2	HPGD	MFHAS1	YWHAE	BICC1	MTUS1	TPX2	HPGD	TOP2A	TPX2	HPGD	ADAR
5	TPX2	DTX3L	EGFR	ANTXR1	TPX2	RSPO3	ITGA2	RSPO3	PTPRF	HPCAL1	DDX60L	EGFR	RSPO3
6	CYP24A1	PARP9	ARHGAP26	KIF14	STAT2	LMCD1	ADAR	HPCAL1	EVA1C	LAMB1	PLSCR1	ZBTB20	EGFR
7	ZFP36L1	MFHAS1	MAP1B	LIMCH1	PNPT1	CALR	ZBTB20	FLNB	CYP24A1	MCM10	MFHAS1	MTUS1	ITGA2
8	HPCAL1	EGFR	LIMCH1	GTSE1	CALR	MAP1B	DCBLD2	ZBTB20	KCNMA1	BICC1	EGFR	HMGA2	MAP1B
9	RSPO3	EIF2S3	HIPK2	ZFP36L1	EVA1C	ZBTB20	HPCAL1	LYPD1	HELLS	RSPO3	TOP2A	AXL	CYP24A1

for cell_type in ['B cells', 'Gamma delta T cells', 'Eosinophils', 'Monocytes']:
    scv.pl.scatter(adata, df[cell_type][:6], ylabel=cell_type, frameon=False, color='cell_type')

scv.tl.rank_dynamical_genes(adata, groupby='condition')
df = scv.get_df(adata, 'rank_dynamical_genes/names')
df.head(5)

ranking genes by cluster-specific likelihoods
    finished (0:00:09) --> added 
    'rank_dynamical_genes', sorted scores by group ids (adata.uns)

	Control	Trait
0	YWHAE	YWHAE
1	CALR	IFI6
2	EVA1C	NLRC5
3	ZFP36L1	EIF2AK2
4	ZBTB20	TPX2

for condition in ['Control', 'Trait']:
    scv.pl.scatter(adata, df[condition][:5], ylabel=condition, frameon=False, color='condition')